eval_forge
What it is
eval_forge is a Python-based evaluation harness for LLMs and AI agents that combines rule-based checks with LLM-as-a-Judge scoring. It uses a cross-vendor strategy (OpenAI for testing, Anthropic for judging) to mitigate self-preference bias and includes a Streamlit dashboard for visualizing results and human calibration.
The problem it solves
Manual evaluation of LLM outputs is not scalable or consistent, and using the same model family for judging introduces self-preference bias.
What you could use it for
- Evaluating factual accuracy of LLM outputs
- Assessing RAG pipeline quality
- Testing model safety and regression
- Comparing performance between different LLM models
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.